prep.measurement | R Documentation |

Prepare the measurement recipe

prep.measurement(values.load, params.load = NULL, values.exo = NULL, params.exo = NULL, values.int = NULL, params.int = NULL, obs.names, state.names, exo.names)

`values.load` |
matrix of starting or fixed values for factor loadings. For models with regime-specific factor loadings provide a list of matrices of factor loadings. |

`params.load` |
matrix or list of matrices. Contains parameter names of the factor loadings. |

`values.exo` |
matrix or list of matrices. Contains starting/fixed values of the covariate regression slopes. |

`params.exo` |
matrix or list of matrices. Parameter names of the covariate regression slopes. |

`values.int` |
vector of intercept values specified as matrix or list of matrices. Contains starting/fixed values of the intercepts. |

`params.int` |
vector of names for intercept parameters specified as a matrix or list of matrices. |

`obs.names` |
vector of names for the observed variables in the order they appear in the measurement model. |

`state.names` |
vector of names for the latent variables in the order they appear in the measurement model. |

`exo.names` |
(optional) vector of names for the exogenous variables in the order they appear in the measurement model. |

The values.* arguments give the starting and fixed values for their respective matrices. The params.* arguments give the free parameter labels for their respective matrices. Numbers can be used as labels. The number 0 and the character 'fixed' are reserved for fixed parameters.

When a single matrix is given to values.*, that matrix is not regime-switching. Correspondingly, when a list of length r is given, that matrix is regime-switching with values and params for the r regimes in the elements of the list.

Object of class 'dynrMeasurement'

Methods that can be used include: `print`

, `printex`

, `show`

prep.measurement(diag(1, 5), diag("lambda", 5)) prep.measurement(matrix(1, 5, 5), diag(paste0("lambda_", 1:5))) prep.measurement(diag(1, 5), diag(0, 5)) #identity measurement model #Regime-switching measurement model where the first latent variable is # active for regime 1, and the second latent variable is active for regime 2 # No free parameters are present. prep.measurement(values.load=list(matrix(c(1,0), 1, 2), matrix(c(0, 1), 1, 2)))

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